Machine Learning for Energy

The energy industry generates massive amounts of data daily. Utilizing data science can produce estimable insights on energy usage and give organizations a decisive advantage.

The next great step for the energy industry is incorporating artificial intelligence and machine learning (AI/ML)

ML can create game-changing applications across the energy landscape such as forecasting energy consumption whether it's natural gas futures or estimating power consumption in a specific grid. Knowing the future demand of a resource provides key actionable information that can be used to lower costs and gain efficiencies.

Within the energy spectrum, renewables show some of the strongest ML use cases. Solar and wind, are in their very nature, variable. With uncontrollable factors such as solar intensity and wind speed, energy foresights become highly desirable. Machine Learning models can make use of unthinkable quantities of data to provide continuous, reliable predictions about future renewable power inconsistencies.

Machine Learning Use Cases

Utility companies have the potential to collect and analyze millions of data points that contain valuable information. Progressive companies have installed Internet of Things (IoT) devices into their products to gather product usage metrics - e.g., a sensor that tracks daily wattage consumption). Once the data is consolidated, it is fed into an ML program where hidden patterns are revealed and insights are unlocked.

Some of the most popular use cases for machine learning applications in the energy industry are listed below:

Anomaly Detection

As data comes in the masses, each data point must be analyzed to ensure the system behaves normally. If faulty behavior were to be detected, an automated notification system is triggered to address the problem immediately.

Anomalies take shape in all sorts of events, some of which could cause immense financial losses. If the system fails or a piece of equipment is malfunctioning, anomaly detection performs in real-time to address the problem immediately.

Demand Forecasting

Demand forecasting is the most popular use-case for machine learning in the energy industry. Sensors and IoT devices track daily energy usage for individual customers. This data is then fed into ML programs to learn each customer's consumption changes and can be used to distribute resources efficiently.

Utility companies can leverage these insights to make profitable decisions and stay ahead of the competition. Furthermore, this information can generate customer-oriented solutions with the optimal contracted capacity - minimizing the cost of expenses.

Price Prediction

Using the power of data science, businesses can aggregate their energy data and build price optimization models. These programs process demand prediction and historical data to make optimized pricing recommendations and help these companies maximize their profit.

Generating an automated solution for selecting the optimal price grants companies a decisive advantage in the industry.

The Challenge

Maintaining a balance between energy supply and demand is possible through ML/AI; however, incorporating machine learning isn't easy. It takes more than a data scientist to produce a working AI program, but rather a team consisting of software engineers, data engineers, and data scientists to build a system for processing data.

Referring to "The Data Science Hierarchy of Needs" diagram, data science business value comes from the top of the pyramid where AI and ML live. Every building block is required for each process above - with every step having the scope of being its very own project.

Now coming back to energy companies, here is the strategy for building a robust real-time system for analyzing energy data:

Solution and Tools


Initially, we need data to run data analysis. Therefore, we must have a reliable way of collecting data (consistency is a must for real-time systems). In the energy world, this process is likely done with sensors that are installed with every product. A software engineer for instrumentation is utilized to build a sensor that can send information for any scenario.

  • Python- Other than being the language of data science, the language allows full flexibility in workflows
  • Airflow- scalable and integrable scheduling system for automating tasks


We can collect the data, but now we must store it in a location where it can be accessed efficiently. Data engineers specialize in building pipelines that funnel the data from collection to the storage and then finally feed the data into the AI model. This is the most critical task that should have all the expertise it needs to accomplish it. The last thing you want to do is to block accessibility and make it challenging to provision the data.

When building a real-time analytical system, your data needs to be stored in an environment that can handle heavyweight tasks such as querying GB of data, cleaning out the data, accessing the data through cost-effective queries, and real-time streaming.

Function as a Service (FaaS) is a solution that meets the above criteria. With having extendable and reliable cloud storage, you can handle real-time processing and ETL (extract, transform, load) processes from the same source.


Once the data has been collected and stored in an accessible environment, it is time to perform data science. This final step is where the business value for the energy industry is produced. The applications here vary on the company's use case; solutions are mentioned above.

The possibilities are endless when you have massive amounts of high-quality, accessible data. Real-time monitoring dashboards can be deployed showcasing the day-to-day energy consumption. The powerful application ingests all of the metric data collected by every scanner and runs analysis that creates insights.

Which grids will consume more energy today? What is the optimal price of a natural gas unit? Are there any systems with malfunctions? Which room in a building will take up more capacity? What time of day will the higher loads take place? These questions can all be answered instantly, and the answers can change as the data is analyzed in real-time.

  • Scikit-learn - the most popular machine learning library for deploy supervised and unsupervised machine learning models.
  • Tensorflow - machine learning and deep learning library that is more heavyweight than scikit-learn, best for deploying neural networks.
  • Pytorch - deep learning library that supports GPU-accelerated computations.
  • Jupyter-Notebook - simple and powerful tool for computing data analysis problems. The majority of data scientists use Jupyter-Notebook
  • Anaconda - powerful all-in-one data science package that includes: Python / R distribution, Jupyter-Notebook, package manager, environment manager. For more information, read our article about the Anaconda Python Distribution.
  • MLflow- machine learning operations framework for having a DevOps orientated philosophy in your predicting applications

Begin to understand your data

Oak-Tree employs a team of experts and specialists that lead organizations to a world where they can understand their data and make decisive decisions. When it comes to the energy industry, Oak-Tree has designed and built powerful, robust systems that get utility companies to their desires of AI. Oak-Tree's expertise in the data landscape can ensure you have the data platform you need.

As a tight, close-knit team, Oak-Tree produces powerful systems to help companies understand and learn from their data.

Ready to begin your data journey?


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